makeFuzzy: Fuzzifying crisp-set data

In cna: Causal Modeling with Coincidence Analysis

Fuzzifying crisp-set data

Description

The makeFuzzy function fuzzifies crisp-set data to a customizable degree.

Usage

makeFuzzy(x, fuzzvalues = c(0, 0.05, 0.1), ...)

Arguments

x	Data frame, matrix, or configTable featuring crisp-set (binary) factors with values 1 and 0 only.
fuzzvalues	Numeric vector of values from the interval [0,1].
...	Additional arguments are passed to configTable.

Details

In combination with allCombs, full.ct and selectCases, makeFuzzy is useful for simulating fuzzy-set data, which are needed for inverse search trials benchmarking the output of cna. makeFuzzy transforms a data frame or configTable x consisting of crisp-set (binary) factors into a fuzzy-set configTable by adding values selected at random from the argument fuzzvalues to the 0's and subtracting them from the 1's in x. fuzzvalues is a numeric vector of values from the interval [0,1].

selectCases can be used before and selectCases1 after the fuzzification to select those configurations that are compatible with a given data generating causal structure (see examples below).

Value

A configTable of type "fs".

See Also

selectCases, allCombs, full.ct, configTable, cna, ct2df, condition

Examples

# Fuzzify a crisp-set (binary) 6x3 matrix with default fuzzvalues.
X <- matrix(sample(0:1, 18, replace = TRUE), 6)
makeFuzzy(X)

# ... and with customized fuzzvalues.
makeFuzzy(X, fuzzvalues = 0:5/10)
makeFuzzy(X, fuzzvalues = seq(0, 0.45, 0.01))

# First, generate crisp-set data comprising all configurations of 5 binary factors that 
# are compatible with the causal chain (A*b + a*B <-> C)*(C*d + c*D <-> E) and, 
# second, fuzzify those crisp-set data. 
dat1 <- full.ct(5)
dat2 <- selectCases("(A*b + a*B <-> C)*(C*d + c*D <-> E)", dat1)
(dat3 <- makeFuzzy(dat2, fuzzvalues = seq(0, 0.45, 0.01)))
condition("(A*b + a*B <-> C)*(C*d + c*D <-> E)", dat3)

# Inverse search for the data generating causal structure A*b + a*B + C*D <-> E from
# fuzzy-set data with non-perfect consistency and coverage scores.
dat1 <- full.ct(5)
set.seed(55)
dat2 <- makeFuzzy(dat1, fuzzvalues = 0:4/10)
dat3 <- selectCases1("A*b + a*B + C*D <-> E", con = .8, cov = .8, dat2)
cna(dat3, outcome = "E", con = .8, cov = .8)

cna documentation built on Aug. 11, 2023, 1:09 a.m.